Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI ideas on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement learning to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating function is its support knowing (RL) step, which was used to refine the model's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's geared up to break down complex queries and reason through them in a detailed way. This assisted reasoning procedure allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, rational reasoning and information interpretation tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective reasoning by routing inquiries to the most appropriate professional "clusters." This approach permits the model to specialize in various problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient models to simulate the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess models against key security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit boost, produce a limitation boost demand and connect to your account group.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and examine designs against key security criteria. You can execute safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The general circulation includes the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
The design detail page supplies essential details about the design's abilities, rates structure, and application standards. You can find detailed usage guidelines, consisting of sample API calls and code snippets for integration. The design supports various text generation jobs, consisting of content production, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning capabilities.
The page likewise consists of release options and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, pick Deploy.
You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, go into a number of circumstances (between 1-100).
6. For Instance type, select your instance type. For optimum efficiency with DeepSeek-R1, bytes-the-dust.com a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might desire to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the deployment is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can experiment with different prompts and adjust model parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for reasoning.
This is an excellent method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The playground provides instant feedback, helping you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for optimum results.
You can quickly check the model in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to carry out inference utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to create text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the method that finest fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design internet browser shows available designs, with details like the supplier name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows crucial details, including:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
5. Choose the model card to see the model details page.
The design details page includes the following details:
- The design name and provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you deploy the design, it's recommended to review the design details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, use the instantly created name or develop a customized one.
- For Instance type ¸ choose an instance type (default: surgiteams.com ml.p5e.48 xlarge).
- For Initial circumstances count, go into the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to deploy the model.
The release procedure can take several minutes to finish.
When release is complete, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run additional requests against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
Clean up
To prevent unwanted charges, complete the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the model using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. - In the Managed implementations area, locate the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the appropriate release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we explored how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies develop innovative options utilizing AWS services and sped up calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference efficiency of big language designs. In his spare time, Vivek delights in hiking, seeing movies, and attempting different cuisines.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer and Bioinformatics.
Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about constructing solutions that help consumers accelerate their AI journey and unlock service worth.